OVERVIEW

In the information age any system or device generates some form of data for diagnostic purposes or analysis.
he course details the techniques for analyzing data in order to extract useful information and knowledge for decision making.

AIMS AND CONTENT

LEARNING OUTCOMES

The teaching unit is designed to equip students with advanced knowledge and skills in the fields of machine learning and data analysis. Building upon foundational concepts, students delve into cutting-edge techniques and methodologies essential for tackling real-world problems in diverse domains. The teaching unit addresses a comprehensive review of fundamental machine learning algorithms, including supervised and unsupervised learning,  and deep learning architectures. Through hands-on exercises and projects, students gain proficiency in implementing these algorithms using popular libraries.

AIMS AND LEARNING OUTCOMES

The student will be able to apply the acquired skills to a case study by deriving the model of the phenomenon that generated the data under analysis.

During the course the following skills will be developed
- personal competence
- social competence
- ability to learn to learn
- competence in project creation
- competence in project management

PREREQUISITES

Coding (Matlab/Python/R), linear algebra, probability and statistics.

TEACHING METHODS

- Frontal lesson (approx. 50% to develop ability to learn to learn)
- Laboratories (approx. 50% to develop personal competence)
- Possibility of a final project in pairs (to develop social competence, competence in project creation, and competence in project management)

For working students and students with certification of Specific Learning Disabilities (SLD), disabilities, or other special educational needs are advised to contact the instructor at the beginning of the course to arrange teaching and examination methods that, while respecting the teaching objectives, take into account individual learning styles.

Students who hold valid certificates relating to Specific Learning Difficulties (SLD), disabilities or other educational needs are invited to contact the lecturer and the school’s disability liaison officer at the start of the course to agree on any teaching arrangements which, whilst respecting the course objectives, take into account individual learning styles. 

The contact details for the university’s disability liaison officer are available at the following link: https://unige.it/commissioni/comitatoperlinclusionedeglistudenticondisabilita. 

SYLLABUS/CONTENT

1. Statistical inference
2. Supervised, Semisupervised, and Unsupervised Learning
3. Statistical Learning Theory
4. Algorithmi Shallow di Machine Learning (esempi in linguaggio Python)
5. Algorithmi Deep di Machine Learning (esempi in linguaggio Python)
6. Generative AI
7. Model Selection and Error Estimation

RECOMMENDED READING/BIBLIOGRAPHY

T. Hastie, R.Tibshirani, J.Friedman "The Elements of Statistical Learning: Data Mining, Inference, and Prediction" 2009.
S. Shalev-Shwartz, S. Ben-David "Understanding machine learning: From theory to algorithms" 2014
C. M. Bishop, H. Bishop. Deep learning: Foundations and concepts. Springer Nature, 2023.
L. Oneto "Model Selection and Error Estimation in a Nutshell" 2020

TEACHERS AND EXAM BOARD

LESSONS

LESSONS START

https://easyacademy.unige.it/portalestudenti/index.php?view=easycourse&_lang=it&include=corso

Class schedule

The timetable for this course is available here: Portale EasyAcademy

EXAMS

EXAM DESCRIPTION

Oral by appointment.

ASSESSMENT METHODS

The student will solve a real problem at will by applying the techniques learned during the course.

FURTHER INFORMATION

Students with valid certifications for Specific Learning Disorders (SLD) may request accommodations for exams at least 7 days prior to the exam date by filling out the “accommodation request form” (available via online services at https://modulionline.unige.it/richiesta-adattamenti# no-back), which will be automatically forwarded by the system to the instructor in charge of the course and to the faculty liaison for students with disabilities and SLDs in their School/Department. 

The student will receive a copy of their request.